Modulating mobile-device displays based on ambient signals to reduce the likelihood of fraud

ABSTRACT

Provided is a process of classifying ambient signals to reduce fraudulent use of information, the process including: receiving, with the mobile computing device, from a remote server, balance-access information by which a stored value card balance can be spent at a point of sale terminal; storing, with the mobile computing device, the balance-access information; sensing, with one or more sensors of a mobile computing device, ambient signals; classifying the ambient signals as indicating the user is in a retail establishment; and in response to the classification, displaying, on a display screen of the mobile computing device, the balance-access information, such that the balance-access information can be input to a point-of-sale terminal.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent claims the benefit of U.S. Provisional Patent Application62/160,811, titled “Dynamic Gift Card Allocation,” filed 13 May 2015,and is a continuation-in-part of U.S. patent application Ser. No.14/839,058, titled “REDUCING THE SEARCH SPACE FOR RECOGNITION OF OBJECTSIN AN IMAGE BASED ON WIRELESS SIGNALS,” filed 28 Aug. 2015, which claimsthe benefit of U.S. Provisional Patent Applications 62/072,044, filed 29Oct. 2014, and U.S. Provisional Patent Applications 62/043,069, filed 28Aug. 2014. The entire content of each of these earlier-filedapplications is hereby incorporated by reference for all purposes.

BACKGROUND

1. Field

The present invention relates generally to limiting access toinformation on mobile computing devices and, more specifically, tomodulating mobile-device displays based on ambient signals to reduce thelikelihood of fraud.

2. Description of the Related Art

In some cases, people obtain valuable goods and services from others inexchange for drawing upon a balance in a stored value card, whichincludes the digital equivalent. Examples include open-loop stored valuecards and closed-loop stored value cards, each of which may take variousforms, such as gift cards, rebate cards, payroll cards, and the like. Inmany cases, the value stored on the card is spent by presenting to aretailer certain information, such as a card number and a pin number.

Recently, online exchanges have arisen where those in possession of suchcards sell the cards, often at a discount relative to the card balance.For example, a user may receive a gift card, as a birthday present, to astore that the user no longer favors, and the user may sell the card, inexchange for cash or another type of card, to the exchange or adifferent user on such an exchange. In some cases, access to the valueof the card is conveyed by sending the card and pin numbers, withouttransferring possession of any physical token, like the card itself.Thus, a buyer of a card may receive information, for example, an email,text, or in-app data, that they can present on their mobile device to aretailer to buy goods or services with the value remaining on the card.In some cases, the user may then re-sell or leave a remaining balance onthe card back to an exchange, or the exchange may authorize the user toonly use a portion of the value stored on the card.

This approach, while relatively convenient for users, can give rise tocertain types of fraud. One consequence of granting access to cards vianetworks, e.g., with mobile computing devices, without transferring aphysical token, is that each party having access to the card (e.g., theseller, the first buyer who spends part of the balance, the second buyerwho spends another part, and so on) could potentially retain theinformation needed to spend the remaining balance on the card, evenafter the card has been sold on the exchange or returned to theexchange. For instance, there is a risk the first buyer could use partof the card's balance, return the card to the exchange, and then spendthe remaining balance on the card before the card is sold again on theexchange (or after the card is sold but before it is used by the secondbuyer).

SUMMARY

The following is a non-exhaustive listing of some aspects of the presenttechniques. These and other aspects are described in the followingdisclosure.

Some aspects include a process of classifying ambient signals to reducefraudulent use of information, the process including: receiving, withthe mobile computing device, from a remote server, balance-accessinformation by which a stored value card balance can be spent at a pointof sale terminal; storing, with the mobile computing device, thebalance-access information; sensing, with one or more sensors of amobile computing device, ambient signals; classifying the ambientsignals as indicating the user is in a retail establishment; and inresponse to the classification, displaying, on a display screen of themobile computing device, the balance-access information, such that thebalance-access information can be input to a point-of-sale terminal.

Some aspects include a tangible, non-transitory, machine-readable mediumstoring instructions that when executed by a data processing apparatuscause the data processing apparatus to perform operations including theabove-mentioned process.

Some aspects include a system, including: one or more processors; andmemory storing instructions that when executed by the processors causethe processors to effectuate operations of the above-mentioned process.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned aspects and other aspects of the present techniqueswill be better understood when the present application is read in viewof the following figures in which like numbers indicate similar oridentical elements:

FIG. 1 illustrates the logical architecture of an example of a gift cardmanagement system in accordance with some of the present techniques;

FIG. 2 illustrates an example of a gift card distribution process thatmay be executed by some embodiments of the system of FIG. 1;

FIG. 3 illustrates an example of a process for reducing fraudulent useof gift cards that may be executed by some embodiments of client devicescommunicating with the system of FIG. 1; and

FIG. 4 illustrates an example of a computer system by which the abovetechniques may be implemented.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Thedrawings may not be to scale. It should be understood, however, that thedrawings and detailed description thereto are not intended to limit theinvention to the particular form disclosed, but to the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the present invention as definedby the appended claims.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

To mitigate the problems described herein, the inventors had to bothinvent solutions and, in some cases just as importantly, recognizeproblems overlooked (or not yet foreseen) by others in the fields ofcomputer security and payment processing systems. Indeed, the inventorswish to emphasize the difficulty of recognizing those problems that arenascent and will become much more apparent in the future should trendsin industry continue as the inventors expect and particularly thoseproblems that cross the boundaries of distinct fields, as is the casehere. Further, because multiple problems are addressed, it should beunderstood that some embodiments are problem-specific, and not allembodiments address every problem with traditional systems describedherein or provide every benefit described herein. That said,improvements that solve various permutations of these problems aredescribed below.

It can be appreciated that while many retail establishments use giftcards (e.g., prepaid cards or vouchers that can be used for purchases atthe establishment, also referred to as stored value cards), numerousinefficiencies exist. For example, individuals may own gift cards from aretailer from which they are not interested in purchasing anything.

Accordingly, described herein in various implementations aretechnologies that enable the centralized exchange of such gift cards.Such cards can be bought and sold at a discount, thereby providingliquidity to the original card owner as well as a discounted purchaseprice (as compared to the original retail price of an item beingpurchased) to the buyer. Additionally, as described herein, thereferenced technologies can provide/maintain a gift card repository andcan enable the efficient utilization of such cards in retail settings(both ‘brick and mortar’ and ecommerce). For example, using a mobileapplication executing on a mobile device (in conjunction with a centralgift card repository/server), a user can utilize gift cards to makeretail purchases (thereby benefiting from the discount associated withthe utilization of otherwise unused gift cards) in substantially thesame amount of time as a conventional retail checkout process wouldtake. In doing so, the user can benefit from the discount associatedwith gift card utilization while maintaining an efficient/seamlesscheckout process/experience.

However, as noted, in many Internet-based use cases, everyone who isgiven access to a gift card (e.g., the information needed to spend), orsome other type of stored-value card, and then returns the card, is inposition to double spend the balance. In many cases, to use the giftcard, the mobile device displays the information used to authorize atransaction on the gift card. As a result, a nefarious user could writedown that information, return the card on an exchange, and then usetheir own record of the information to draw down the card's balancebefore another authorized use by another person.

This problem is unique to the Internet age because copies of theinformation are shared, potentially anonymously, over networks, and theease of transacting results in balances being sliced more finely,putting the card information potentially in the possession of severaluntrusted parties. Further, users of these systems often expect aseamless experience, and slow, cumbersome authentication schemes areoften not commercially feasible. Compounding these challenges, operatorsof gift card exchanges are often not in a position to dictate technicalspecifications of the point-of-sale terminals or transaction processingsystems by which the cards are spent. As a result, efforts to mitigatefraud often need to accommodate legacy point-of-sale and transactionprocessing systems.

Some embodiments described below may limit the user's ability to obtainthe sensitive card information, e.g., the card number or pin number.Some embodiments may probabilistically classify ambient signals asindicating whether the user is likely at a place where there is alegitimate use of the card, e.g., at a retail store that accepts thecard, and in some cases, at certain types of retail stores. The resultof the classification may be used to determine whether to display thecard information on a display screen of a mobile computing device. Forinstance, the card information may remain un-displayed and encrypted onthe user's mobile computing device until the user 1) requests to use thecard; and 2) ambient signals are classified as indicative of legitimateuse. In contrast, users who attempt to view the card information whenthere is no legitimate use likely, like viewing card numbers or pinnumbers while at home with a card only usable for in-store transactions,may be prevented from viewing the card's information.

FIG. 1 depicts an illustrative system architecture 100, in accordancewith one implementation of the present disclosure. The systemarchitecture 100 includes one or more user devices 102, merchant devices104, and server machine 120. These various elements or components can beconnected to one another via network 110, which can be a public network(e.g., the Internet), a private network (e.g., a local area network(LAN) or wide area network (WAN)), or a combination thereof.Additionally, in certain implementations various elements maycommunicate and/or otherwise interface with one another (e.g., userdevice 102 with merchant device 104). The various illustrated computingdevices may be formed with one or more of the types of computer systemsdescribed below with reference to FIG. 4.

User device 102 can be a rackmount server, a router computer, a personalcomputer, a portable digital assistant, a mobile phone, a laptopcomputer, a tablet computer, a camera, a video camera, a netbook, adesktop computer, a media center, a smartphone, a watch, a smartwatch,an in-vehicle computer/system, any combination of the above, or anyother such computing device capable of implementing the various featuresdescribed herein. Various applications, such as mobile applications(‘apps’), web browsers, etc. (not shown) may run on the user device(e.g., on the operating system of the user device). It should beunderstood that, in certain implementations, user device 102 can alsoinclude and/or incorporate various sensors and/or communicationsinterfaces (not shown). Examples of such sensors include but are notlimited to: accelerometer, gyroscope, compass, GPS, haptic sensors(e.g., touchscreen, buttons, etc.), microphone, camera, etc. Examples ofsuch communication interfaces include but are not limited to cellular(e.g., 3G, 4G, etc.) interface(s), Bluetooth interface, WiFi interface,USB interface, NFC interface, etc.

Merchant device 104 can be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a mobile phone, alaptop computer, a tablet computer, a camera, a video camera, a netbook,a desktop computer, a media center, a smartphone, a watch, a smartwatch,an in-vehicle computer/system, a point of sale (POS) system, device,and/or terminal, any combination of the above, or any other suchcomputing device capable of implementing the various features describedherein. Various applications, such as mobile applications (apps'), webbrowsers, etc. (not shown) may run on the merchant device (e.g., on theoperating system of the merchant device). It should be understood that,in certain implementations, merchant device 104 can also include and/orincorporate various sensors and/or communications interfaces (notshown). Examples of such sensors include but are not limited to:accelerometer, gyroscope, compass, GPS, haptic sensors (e.g.,touchscreen, buttons, etc.), microphone, camera, barcode scanner, etc.Examples of such communication interfaces include but are not limited tocellular (e.g., 3G, 4G, etc.) interface(s), Bluetooth interface, WiFiinterface, USB interface, NFC interface, etc. It should be understoodthat in certain implementations merchant device 104 may be a dedicatedPOS terminal (e.g., including an integrated barcode scanner) while inother implementations merchant device 104 may be a handheld or personalcomputing device (e.g., smartphone, tablet device, personal computer,etc.) configured to provide POS functionality (whether utilizing thefunctionality provided by the various components/sensors of the deviceor via one or more connected peripherals). It should also be understoodthat, in certain implementations, merchant device 104 can be a server,such as a webserver that provides an ecommerce site/service, such as maybe accessed by user device 102 via a website and/or dedicatedapplication.

Server machine 120 can be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a mobile phone, alaptop computer, a tablet computer, a camera, a video camera, a netbook,a desktop computer, a smartphone, any combination of the above, or anyother such computing device capable of implementing the various featuresdescribed herein. Server machine 120 can include components such as giftcard allocation engine 130, and gift card repository 140. The componentscan be combined together or separated in further components, accordingto a particular implementation. It should be noted that in someimplementations, various components of server machine 120 may run onseparate machines (for example, gift card repository 140 can be aseparate device). Moreover, some operations of certain of the componentsare described in more detail below.

Gift card repository 140 can be hosted by one or more storage devices,such as main memory, magnetic or optical storage based disks, tapes orhard drives, NAS, SAN, and so forth. In some implementations, gift cardrepository 140 can be a network-attached file server, while in otherimplementations gift card repository 140 can be some other type ofpersistent storage such as an object-oriented database, a relationaldatabase, and so forth, that may be hosted by the server machine 120 orone or more different machines coupled to the server machine 120 via thenetwork 110, while in yet other implementations gift card repository 140may be a database that is hosted by another entity and made accessibleto server machine 120. Gift card repository 140 can store informationrelating to various gift cards, such as codes, bar codes, and/or anyother such identifiers, as well as information relating to such cards(e.g., monetary value, expiration date, usage restrictions, etc.).

It should be understood that though FIG. 1 depicts server machine 120and devices 102 and 104 as being discrete components, in variousimplementations any number of such components (and/or elements/functionsthereof) can be combined, such as within a single component/system. Forexample, in certain implementations devices 102 and/or 104 canincorporate features of server machine 120.

As described in detail herein, various technologies are disclosed thatenable dynamic gift card allocation. In certain implementations, suchtechnologies can encompass operations performed by and/or in conjunctionwith gift card allocation engine 130.

FIG. 2 depicts a flow diagram of aspects of a method 200 for dynamicgift card allocation. The method is performed by processing logic thatmay comprise hardware (circuitry, dedicated logic, etc.), software (suchas is run on a general purpose computer system or a dedicated machine),or a combination of both. In one implementation, the method is performedby one or more elements depicted and/or described in relation to FIG. 1,while in some other implementations, one or more blocks of FIG. 2 may beperformed by another machine or machines.

For simplicity of explanation, methods are depicted and described as aseries of acts. However, acts in accordance with this disclosure canoccur in various orders and/or concurrently, and with other acts notpresented and described herein. Furthermore, not all illustrated actsmay be required to implement the methods in accordance with thedisclosed subject matter. In addition, those skilled in the art willunderstand and appreciate that the methods could alternatively berepresented as a series of interrelated states via a state diagram orevents. Additionally, it should be appreciated that the methodsdisclosed in this specification are capable of being stored on anarticle of manufacture to facilitate transporting and transferring suchmethods to computing devices. The term article of manufacture, as usedherein, is intended to encompass a computer program accessible from anycomputer-readable device or storage media.

At block 210, a location of a device (e.g., user device 102) can bedetermined. At block 220, a first gift card can be presented at thedevice, such as in a manner described herein. In certainimplementations, such a gift card can be presented based on the location(e.g., the location of the device determined at block 210). At block230, it can be determined that the first gift card has been utilized ina transaction. At block 240, a second gift card can be provided at thedevice. In certain implementations, such a gift card can be providedbased on a determination that the first gift card (e.g., the gift cardpresented at block 220) has been utilized in the transaction. Variousaspects of the referenced operations are described and illustrated ingreater detail herein.

By way of further illustration, an application (‘app’) executing on userdevice 102 can request or otherwise determine a current location of thedevice (e.g., based on GPS coordinates, etc.). Based on the determinedlocation, information regarding one or more retail establishments (e.g.,those within a defined proximity to the current location) can berequested/retrieved and presented at the device. In certainimplementations, such information can reflect those proximate retailestablishments with respect to which gift cards are available (e.g., atgift card repository 140 of server machine 120).

Upon receiving a selection (e.g., by the user) of a particular retailestablishment, one or more gift cards (e.g., barcodes, etc., stored ingift card repository 140) can be requested/received by the user device.It should be noted, however, that in certain implementations such giftcard information may be requested/received upon determining the locationof the device (e.g., by requesting gift cards for those retailestablishments that are proximate to the device, maintaining such cardsin memory, and presenting them upon receiving a selection by the user ofa particular establishment).

In certain implementations, an input can be received (e.g., as providedby the user via the device) that reflects a sale/purchase amount (e.g.,the total amount charged by the retailer for a particular purchase,e.g., ‘$54.63’). Upon receiving such a selection, one or more gift cardscan be received, requested, and/or selected (e.g., from those gift cardspreviously received and stored in the memory of the user device). Incertain implementations, such gift cards can be selected/requested(e.g., from gift card repository 140) based on any number of factors,such as the degree to which the credit amount of the gift cardapproximates/corresponds to the sale/purchase amount. Moreover, incertain implementations the user may be provided with the option toselect whether to utilize relatively more gift cards (e.g., of smallerincrements, together which add up to the total purchase price, therebyreceiving a larger discount), or relatively fewer gift cards (some ofwhich may be of larger increments, thereby necessitating the use of asmaller number of gift cards and providing a more expedient check outprocess). Additionally, in certain implementations variouspredictions/projections can be computed with respect to thesale/purchase amount (e.g., based on the retail establishment, theuser's purchase history, the amount of time the user has spent in thestore, the distance the user has traveled in the store, theareas/departments of the store that the user has visited, etc.), and oneor more gift cards can be selected/provided based on suchpredictions/projections.

Moreover, in certain implementations a gift card having a value thatexceeds the total sale/purchase amount may be selected/provided. Uponcompletion of the transaction, the remaining balance on the gift cardcan subsequently be provided (e.g., to another user) with respect toanother transaction. By way of illustration, a first user may initiate atransaction totaling $60 and a gift card having $100 worth of credit maybe selected/provided (e.g., in a manner described herein) in order tocomplete the transaction (leaving the gift card with $40 worth ofcredit). Subsequently, a second user may, for example, initiate atransaction totaling $40 and the same gift card (now having $40 worth ofcredit) may be selected/provided (e.g., in a manner described herein) inorder to complete the transaction (thereby utilizing the entireremaining value on the gift card). In doing so, a single gift card canbe utilized by different users at different times for differenttransactions. Moreover, in certain implementations each user will onlybe required to pay or otherwise account for the portion/increment of thegift card utilized for his/her purchase. Additionally, in certainimplementations, once a particular gift card is utilized in a firsttransaction, such a gift card may be temporarily held (e.g., for adefined period of time and/or until a confirmation of the originaltransaction and/or the current balance of the card isreceived/determined) prior to providing the card again for a subsequenttransaction. In doing so, the remaining balance on the card can, forexample, be confirmed prior to providing it in another transaction.

Additionally, in certain implementations the referenced gift card(s) canbe provided to the user device in advance of charging, debiting, etc.,the requesting user for the value of the card. That is, as noted above,it can be appreciated that while a gift card of a particular total value(e.g., $100) may be selected/provided in order to complete thetransaction, in many scenarios the user may only use a portion of thetotal value of the card (e.g., $60). As such, in lieu of charging theuser the full value of the card (e.g., $100), the gift card can insteadbe selected/provided (e.g., before the transaction has been completedand without initially charging/debiting the requesting user for the fullvalue of the card), and once the transaction is complete the user can becharged/debited for the increment used during the transaction (e.g.,based on the total purchase price as provided by the user, anindependent verification of the gift card balance, etc.) while theremaining value on the gift card can be utilized in subsequenttransaction(s) (e.g., by other users), such as in a manner describedherein.

The various selected/received gift cards can then be sequentiallypresented/provided, e.g., on the screen of the user device. The userdevice (e.g., a smartphone) can be placed or otherwise oriented inrelation to the merchant device (e.g., in relation to the barcodescanner of a POS terminal) such that the merchant device can scan, read,or otherwise perceive or capture the code/barcode of the gift card beingpresented. In doing so, the user can complete the retail transactionusing gift cards originating at server machine 120. Moreover, in certainimplementations a comparable/related technique can be employed withrespect to coupons. For example, in certain implementations variouscoupons can be presented on the screen of the device in a sequence suchthat they can be received/processed by the merchant device insuccession.

It should be understood that in scenarios in which multiple gift cardsare to be utilized, such gift cards can be provided sequentially in anynumber of ways. For example, in certain implementations feedback can beprovided/received (e.g., provided by the user to the device, such as byswiping a touch screen or pressing a button) which indicates thatanother gift card is to be presented. It should also be noted that, incertain implementations, feedback may be provided/received, indicatingthat a particular gift card did not work (in which case a replacementcard can be retrieved/provided).

By way of further example, in certain implementations various sensoryinputs can be received and processed by the user device which can bedetermined to indicate that a presented gift card has been processed andthat a subsequent gift card is to be presented (if relevant/necessary).By way of illustration, in certain implementations various audio inputs(e.g., a ‘beep’ or tone emitted by the merchant device, indicating thata barcode has been scanned) can be received by the user device (e.g., byan integrated or external microphone), and such inputs can be processedto determine that the presented gift card has been processed (and thatanother gift card, if necessary, is to be presented). By way of furtherillustration, in certain implementations various visual/optical inputs(e.g., a flash or pulse of the barcode scanner of the merchant device,indicating that a barcode has been scanned) can be received/perceived bythe user device (e.g., by an integrated ‘front facing’ camera), and suchinputs can be processed to determine that the presented gift card hasbeen processed (and that another gift card, if necessary, is to bepresented). By way of yet further illustration, in certainimplementations various motion inputs (e.g., a rotation/orientation ofthe user device, indicating that a barcode has likely been scanned) canbe identified by the user device (e.g., via an integrated accelerometer,gyroscope, etc.), and such inputs can be processed to determine that thepresented gift card has likely been processed (and that another giftcard, if necessary, is to be presented). It should also be noted that,in certain implementations, various aspects of the timing of thepresentation of the referenced gift cards can also be accounted for,such that, for example, upon presenting a particular gift card for adefined time interval (e.g., 10 seconds), another gift card can beselected/requested and displayed.

As noted, certain users may attempt to fraudulently/improperly use thedescribed technologies, such as by capturing/recording gift cards thatare presented in order to utilize them at a later time. Accordingly, inorder to ensure that presented gift cards are likely to be utilized inlegitimate retail scenarios, various determinations can be made, basedon which a score can be computed, reflecting the likelihood that thecard is (or is not) being used fraudulently. For example, one or moreinputs from various motion sensors (e.g., accelerometer, gyroscope,etc.) can be received and processed in order to determine themanner/pattern in which the user device is being maneuvered. A userdevice that is presenting the gift cards legitimately (e.g., in a retailsetting) is likely to exhibit a consecutive series ofmovements/rotations (e.g., placing the device face down, followed by arotation of the device such that it is face up, followed by anotherrotation to face down, etc.), while a device that is being usedinappropriately (such that, for example, card numbers/codes are beingrecorded by the user) is less likely to exhibit such motion (as the useris likely to simply hold the device in place and cycling throughmultiple cards). Accordingly, in certain implementations the user deviceand/or the referenced app executing thereon can be configured topresent/display the referenced gift cards/barcodes while the device isdetermined to be positioned in a particular orientation (e.g., facedown, as the device is likely to be oriented when the barcode is beingscanned), while not presenting (or obscuring) such cards/codes when thedevice is not so oriented. In doing so, the card/code can be presentedwhen being legitimately used/scanned while not being presented in otherorientations which may otherwise enable improper usage. By way offurther example, one or more audio inputs can be received (e.g., by anintegrated or external microphone) and processed in order to determinean amount/level of sound/noise (e.g., ambient noise) perceptible to thedevice. In scenarios in which the user device is presenting the giftcards legitimately (e.g., in a retail setting), a certain degree ofambient noise (and/or various sounds, talking, beeps, etc.) is likely tobe perceptible, while with respect to a device that is being usedinappropriately (such that, for example, card numbers/codes are beingrecorded by the user), such audio inputs/noise are less likely to beperceived. By way of yet further example, one or more visual inputs canbe received (e.g., as captured by one or more integrated cameras) andprocessed in order to identify/determine various aspects of thesurroundings of the device. In scenarios in which the user device ispresenting the gift cards legitimately (e.g., in a retail setting),various elements, characteristics, etc. (and changes thereto), arelikely to be perceptible, while with respect to a device that is beingused inappropriately (such that, for example, card numbers/codes arebeing recorded by the user), such visual elements, characteristics, etc.are relatively less likely to be perceived/identified. In yet otherexamples, various aspects of the location of the user device can beaccounted for in determining the likelihood that the presented giftcards are (or are not) being used legitimately. For example, utilizationof the referenced application in an area that is determined to beresidential (and/or is not determined to be a retail location) canindicate that the usage is more likely to be improper.

Some embodiments may automatically check balances for sold andsurrendered cards to ascertain whether a card that has been surrenderedhas had the card balance change, possibly indicating fraud by a user whocopied the information on the card. In some cases, the balance checksmay be performed through automated interaction with a telephone menu(e.g., by synthesizing appropriate key presses or voice responses andemitting corresponding audio). In some cases, the rate of such checksmay be modulated responsive to use of a card, e.g., the rate of checksmay be elevated for a threshold amount of time after a card is returned.Some embodiments may perform such checks while a user purports to be ina store, e.g., in response to the user requesting a card or crossing ageofence, to ascertain whether the card was in fact used for a purchase.In some cases, some embodiments may flag a transaction as potentiallyindicative of fraud in response to a user requesting a card and thebalance not changing within a threshold duration of time.

Upon completion of the transaction (e.g., when enough gift cards havebeen presented by the user device to cover the cost of the purchase),feedback/input can be received by the user device (as provided, forexample, by the user) indicating that the transaction is complete (atwhich point additional gift cards will not be displayed). Moreover, incertain implementations, various aspects of the location of the devicecan be used in determining that the transaction is complete (and thatadditional gift cards are not to be displayed). For example, upondetermining that the device has traveled beyond a certain distance(e.g., 50 feet) from the area in which the transaction was initiated(and/or from the location of the retail establishment), it can befurther determined that the transaction is likely to be complete andadditional gift cards will not be presented.

Some embodiments may limit access to a threshold amount of cards, orcards having an aggregate balance, based on statistical distribution ofcart values for a particular retail store. For instance, someembodiments may obtain transaction records for each of a set of stores,and for each store, some embodiments may calculate population or samplestatistics indicative of a measure of central tendency (e.g., mean,mode, or median) and a measure of variability (e.g., a variance,standard deviation, etc.). Some embodiments may infer a threshold amountabove which cart values for a particular store are expected to be veryunlikely, e.g., accounting for less than 1/100, less than 1/1,000, orless than 1/10,000 of the transactions at a retail store. Someembodiments may select cards to be sent to a user based on whether thosecards, either individually or in the aggregate, contain a balanceexceeding this threshold, rejecting cards that would cause the thresholdto be exceeded.

As noted above, in order to prevent fraudulent/improper use of thedescribed technologies, various forms of verification/authentication canbe incorporated. For example, in certain implementations, in order toutilize the described technologies, the user may be prompted to log inor otherwise associate their gift card usage with a third party login,service, account, etc.

In certain implementations, server machine 120 and/or gift cardallocation engine 130 can be configured to select/provide various giftcards based on any number of factors. For example, with respect to giftcard sellers that have been determined to be relatively more likely tosell/provide gift cards that may not work, gift cards that are providedby such sellers can be prioritized (e.g., provided to a requesting useras soon as possible), as the more time elapses from the time of sale ofthe card, the greater the likelihood that the card may not work. By wayof further example, with respect to new gift card sellers, gift cardsthat are provided by such sellers can be prioritized (e.g., provided toa requesting user as soon as possible), in order to provide such sellerswith quicker payment for the cards they provide.

It should also be noted that while the much of the foregoing descriptionhas illustrated various aspects of the described technologies inrelation to utilizing mobile devices in retail transactions (e.g., inconjunction with a POS terminal), in certain implementations thereferenced technologies can also be implemented in ecommerce settings(e.g., in conjunction with a web browser). For example, via a browserplugin (and/or any other such application, module, etc.), upondetermining that a user is checking out of an ecommerce site (e.g.,finalizing/executing an ecommerce transaction), various aspects of thewebpage/‘shopping cart’ can be processed/analyzed. In doing so, thefinal purchase price can be determined and one or more gift cards can beprovided/presented within the checkout interface. In doing so, the usercan complete the ecommerce transaction while availing themselves ofsavings attendant with paying via gift cards.

It should also be noted that while the technologies described herein areillustrated primarily with respect to dynamic gift card allocation, thedescribed technologies can also be implemented in any number ofadditional or alternative settings or contexts and towards any number ofadditional objectives.

FIG. 3 illustrates an example of a process 300 to selectively determinewhether to display balance-access information for stored value cardsbased on the output of classifiers that determine whether the mobilecomputing device executing the process 300 is likely in a retail store.As noted, in some cases, some gift card exchanges may send buyers ofgift cards balance access information for those gift cards, such as agift card number and a pin number. In some cases, users may attempt toengage in fraudulent transactions by purchasing gift cards, recording intheir personal records the balance-access information, and thenreturning the gift card back to the exchange, for instance, by sellingthe gift card back to the exchange or by only using a portion of thebalance of a gift card that is automatically returned. Such users,wishing to engage in fraud, may then use the balance-access informationto engage in a subsequent transaction, before the subsequent legitimateuser of the gift card can use the remaining balance. For instance, theuser may present online or in person the information in their personalrecord of the balance-access information to spend gift-card value thatthey surrendered to the exchange. To make such endeavors lessattractive, some embodiments may selectively display the balance-accessinformation in response to determining that the user is likely in alocation at which they would have a legitimate reason for accessing suchinformation, for example, at a point-of-sale terminal in a retail storethat accepts the card.

In some cases, access may be granted in response to determining that theuser has crossed a geo-fence associated with a retail store. However,such techniques, while consistent with some embodiments, may leave opensome relatively easy avenues for exploitation, for instance, byaccessing the information while in the user's car in the parking lot ofthe store. In some cases, satellite navigation and geolocation servicesavailable on mobile devices are often unreliable and imprecise forindoor positioning, particularly for determining whether the user iswithin a relatively close distance to point-of-sale terminal, likewithin 3 to 5 meters, or less. Accordingly, some embodiments may usesignals from a variety of sensors to ascertain, based on the mobilecomputing device's current environment, whether the mobile computingdevice is likely being used in a legitimate transaction, or whether themobile computing device is likely being accessed simply to view andrecord the balance-access information improperly. Some embodiments mayengage in this routine in a relatively battery-friendly way, using acombination of ambient signals that collectively yield a relatively lowfalse positive and low false negative rate, thereby providing arelatively seamless experience for the end-user. Further, someembodiments may accommodate a diverse array of types of point-of-saleterminals, including legacy systems that are not specifically configuredto address these problems.

In some embodiments, the process 300 may be executed by a mobilecomputing device, such as a tablet computer, wearable computing device,cell phone, or the like, for instance, a hand-held mobile computingdevice having a battery. In some embodiments, the mobile computingdevice may have a suite of sensors, such as a microphone, one or morecameras, a light-intensity sensor, a time-of-flight sensor, an inertialmeasurement unit, a magnetometer, a satellite navigation signalreceiver, and one or more radios, like a Bluetooth radio, a near-fieldcommunication radio, a Wi-Fi™ radio, and a cellular radio. Certaincombinations of these sensors may produce signals that can be relativelyreliably classified as indicating whether a user is in particular aretail store near a point-of-sale terminal. In some embodiments, themobile computing device may have the features of the computing devicedescribed below with reference to FIG. 4, and in some embodiments, themobile computing device may communicate via the net Internet with theabove-described gift card management service, for instance, in thecourse of performing the process 200 of FIG. 2.

In some embodiments, the process 300 begins with receivingbalance-access information by which a stored value card balance (alsoreferred to as a gift card, but not limited to gift cards) can be spentat a point-of-sale terminal, as indicated by block 302. In some cases,this information may be obtained upon (e.g., in response to) a userrequesting with a web browser or native mobile application a gift cardusable at a particular retailer, for instance, from the above-describedgift card management service of FIG. 1.

In some embodiments, this information may be received via email, text,or an API response, like an HTTP request. In some cases, the receivedbalance-access information is received in encrypted form, for instance,as an additional layer of encryption underneath encryption used toconvey the information over the Internet, such as a layer under SSL orTLS encryption. For example, the balance-access information may bestored in an AES 256 encrypted blob that is sent over the Internet via aTLS encrypted communication. In some embodiments, the balance-accessinformation may be stored in encrypted form, such that a userinterrogating program state or memory of the mobile computing device isunable to view the balance-access information. In some embodiments, thebalance-access information is sent as a string, for example, a gift cardnumber and a pin number. In some embodiments, the balance-accessinformation is sent in the form of an image, such as a barcode image orQR code image that, upon being displayed on a display screen of themobile computing device, can be scanned by a scanner at a point-of-saleterminal to enter the gift card information.

Next, some embodiments may store the received balance-accessinformation, as indicated by block 304. As noted above, in some cases,this information may be stored in encrypted form on the client mobilecomputing device, for instance, after decrypting a TLS encryptedcommunication, leaving the encrypted blob in memory, without yetdecrypting the AES 256 encryption. In some cases, decryption keys may bestored in obfuscated memory of the mobile computing device, forinstance, distributed among several variables of source code by which anative mobile application is written, such that efforts to decompile orotherwise analyze compiled source code are less likely to reveal thedecryption keys.

Next, some embodiments may determine whether the user requests to usethe card, as indicated by block 306. In some cases, a user may requestthe card before step 302, and it should be generally noted that thesteps described herein are not limited to the order in which the stepsare displayed or described. In some embodiments, the user may request touse the gift card through multiple steps, for instance, by requestinggift cards for a particular retailer in a first request, for example, ina request to a native mobile application or webpage that securesresponsive balance-access information from the gift card managementservice, and then later by the user selecting an input in a userinterface of the webpage or native mobile application that indicates theuser wishes to display the information for entry to a point-of-saleterminal. In some cases, a native mobile application or web page mayhave an event handler associated with a region of a display screen, andthat event handler may detect an on-press event and, in response,advance the routine to additional steps of process 300, for instance, ina press-to-display user interface.

In some cases, a multi-region press may be requested or required, forinstance, with two fingers of the user's left hand on one side of thescreen and two fingers of the user's right hand on the other side of thescreen, such that some inferences about the likely orientation of thescreen relative to the user may be drawn by the native mobileapplication, particularly when combined with readings from an inertialmeasurement unit, as described below. For example, in some cases, thenative mobile application may respond to such a multitouch input upondetermining that the orientation of the screen is vertical, as would bethe case when a user is holding the screen between their left and rightfore fingers and thumbs vertically, with the screen oriented away fromthe user, toward a retail sales clerk viewing the screen to enter thebalance access information into a point-of-sale terminal. Or, in somecases, a user may merely engage in input indicating the user wishes touse the card, and upon the user releasing a touch, the process mayproceed.

Upon determining that the user does not yet request to use the card,some embodiments may continue to wait, or, upon determining that theuser does request use the card, embodiments may proceed to the nextstep.

Next, some embodiments may sense ambient signals, as indicated by block308. In some cases, waiting to sense ambient signals until the userrequests to use the card may reduce battery drain associated withconstantly monitoring such signals, for instance, even when the user isnowhere near a retail store or has shown no intent to use a gift card.(Or some embodiments may constantly monitor such signals to provide amore responsive experience at the expense of power consumption.) In somecases, sensing ambient signals may be triggered by one of the two stagesof a request to use the card described above, for instance, in responseto a user requesting gift cards associated with a given retailer. Insome embodiments, sensing of ambient signals may have a timeoutthreshold at which point sensing may cease to protect the battery of themobile computing device, and a user may be presented with an input bywhich the user can indicate an intent to continue attempting to use agift card.

A variety of different types of signals may be sensed with a variety ofdifferent types of sensors on the mobile computing device. In somecases, some of the signals or sensed without regard to whether the userrequest use the gift card, while other signals, particularly morebattery intensive sensors are engaged responsive to a user request. Insome embodiments, the sensor is a radio of the mobile computing device,and the signal is a wireless beacon, such as a Wi-Fi beacon or aBluetooth beacon, or an NFC identifier. In some embodiments, a beaconidentifier encoded in the beacon may be compared against a list ofidentifiers associated with retail stores at which the gift card may belegitimately used, and a wireless environment score may be calculatedbased on the result of this comparison. For instance, a binary score ofone may be output in response to detecting a match, indicating the useris within range of a beacon known to be in a store at which a gift cardin memory is usable. In some cases, different scores may be calculatedfor different gift card stored in memory, as different gift cards may beassociated with different types of retail stores and associated ambientenvironments.

In some cases, users may attempt to spoof such beacons, for instance, byprogramming the SSID of their home wireless router to match thatbroadcast at a store to trick systems relying on WiFi™ beaconinformation, or by configuring Bluetooth™ beacons to broadcast spoofedUUIDs. To frustrate such attacks, some embodiments may sense a rollingencrypted code broadcast in the beacon and determine whether thatrolling encrypted code matches an expected current state for a beaconassociated with such a retailer. In some cases, wireless beacons maybroadcast a rolling encrypted code with a linear shift registeralgorithm, or with other techniques, like with a KeeLoq™ code. In someembodiments, matching of codes may be determined with one or more remotecomputing devices, such as via a request to the gift card managementservice described above, which may compare beacon identifiers to anindex of beacon identifiers and respond with a store identifier orbinary signal indicating a match. Or in some cases, the gift cardmanagement service or the mobile computing device may send a rollingencrypted beacon identifier to a third party server, which may respondwith a store identifier.

In some embodiments, wireless radio signals may generally determine thegeolocation of the mobile computing device, and classifiers for one ormore other types of signals may be obtained in response. For example,classifiers for a set of features known to be associated with a givenretail store may be downloaded and stored in cache memory in response tothe mobile computing device crossing a geo-fence associated with thatstore. In another example, a set of classifiers may be sent to, andstored by, the mobile computing device in response to a particular giftcard being sent to the device, for instance, a set of classifierscorresponding to a particular retailer at which the sent gift card isusable. As a result, relatively granular and store specific classifiersmay be configured without storing such classifiers for every store towhich a user may visit. In some embodiments, the techniques described inU.S. patent application Ser. No. 14/839,058, titled “REDUCING THE SEARCHSPACE FOR RECOGNITION OF OBJECTS IN AN IMAGE BASED ON WIRELESS SIGNALS,”filed 28 Aug. 2015 may be used to this effect.

In another example, the sensed ambient signals may be audio signalssensed with a microphone of the mobile computing device. In some cases,the sensed signals may be time-series signals, such as an amplitude ofaudible signals that varies over time. In some cases, multiplemicrophones on the mobile computing device may sense multiple audiofeeds, and those audio feeds may be used in subsequent steps todetermine a directionality of signals.

In another example, a light intensity sensor on the mobile computingdevice may sense time varying light intensity in the environment of themobile computing device, for instance, from overhead lighting. In somecases, location signals may be embedded in these fluctuations in theintensity of overhead lighting, or in some cases, different types oflighting may emit useful signals, like 120 Hz oscillations offluorescent lights, that provide an additional signal by which locationmay be determined.

In another example, a camera of the mobile computing device may capturean image or sequence of images. In some embodiments, both a front facingand rear facing camera of the mobile computing device may captureimages, for instance, to ascertain whether a screen of the mobilecomputing device is pointed towards a point-of-sale terminal and that arear facing camera is pointed to a face of the user, therebydemonstrating that the user is less likely to be able to viewinformation on the screen. In some cases, an array of cameras on oneface of the mobile computing device may capture images, andcomputational photography techniques may be used to ascertain spatialinformation based upon a light field impending upon the mobile computingdevice. In another example, the mobile computing device may have atime-of-flight sensor by which a scan of 3-D surfaces is obtained, forinstance, providing in combination with an image sensor, both a pixelintensity and pixel distance.

In another example, the mobile computing device may have a magnetometer,which may sense a time varying magnetic field and orientation of themagnetic field, such as the user moves through the store, or as mayoccur due to electromagnetic signals emitted by the operation ofcircuitry in a point-of-sale scanner. Variations in such signals, eitherin time or space, may be classified as indicating presence at a point ofsale terminal.

In some cases, the mobile computing device may include an inertialmeasurement unit, such as a six axis accelerometer operative to sensechanges in rotational velocity at about three orthogonal axes and thechanges in linear translation speed about three orthogonal axes. In somecases, the inertial measurement unit may be operative to sense adownward direction due to acceleration from gravity. In some cases, theinertial measurement unit may output a multidimensional time series,such as a sequence of six dimensional values indicating sensor readingsat each of six dimensions at each instance the inertial measurement unitis polled by a native mobile application.

Next, some embodiments may classify the ambient signals as indicatingthe user is in a retail establishment by determining a classificationscore, as indicated by block 310. In some cases, the classificationscore is a weighted combination of a plurality, e.g., two, three, four,five or more, sensor-specific classification scores. In some cases,these weights may be dynamically adjusted over time, for instance, inresponse to detected miss classifications to reduce a misclassificationrate. For example, some embodiments may implement a stochastic gradientdescent algorithm to reduce an amount of error on a training set ofsensor signals labeled with values indicating whether the collection ofsignals correspond to a fraudulent use or a legitimate use for instanceat a given retail store, such that store-specific sets of parameters maybe downloaded upon determining that the user has crossed a geo-fenceassociated with the store.

In some embodiments, audio signals may be classified by calculating anaudio classification score. In some cases, the audio signal may benormalized, for instance, by amplifying or suppressing the signal toreach a target root mean square value or maximum value in amplitude. Insome embodiments, features may be extracted from the normalized signal.A variety of different types of features may be extracted. For instance,some embodiments may pass the normalized signal through one or morebandpass filters, and responses exceeding a threshold amplitude outputfrom the bandpass filters may be designated as a feature. In some cases,the features may be two-dimensional features corresponding to both aduration and an indication that an output exceeding the thresholdoccurred. In some cases, such a feature may correspond to a beep soundemitted by a point-of-sale terminal known to be used by the merchant,for example, as the sales clerk scans items and the system beeps. Insome cases, the sound of these beeps may be used as a signal indicativeof the presence of a point-of-sale terminal. In another example, someembodiments may extract features by executing a Fourier analysis on theaudio signal and extracting features from portions of the output thatexceed some threshold duration or amplitude. In another example, somestores may embed location identifiers in in-store audio, and someembodiments may extract those identifier from the audio, for example, bytime or frequency demultiplexing the audio signal. In some cases,classification models for the different audio signals may be storespecific, and those models may be downloaded based on crossing ageo-fence associated with the store or downloading a card associatedwith the store. In another example, certain signals of relativelyconstant duration may be detected with a convolution layer of a neuralnet that convolve a kernel over time to classify whether a trailingduration of the audio signal includes a beep of a point-of-sale terminal(or other indicative signal). In some cases, such models may be trainedby sampling audio and labeling sample audio as indicating a legitimatetransaction or a fraudulent transaction, for example, based on loggedsensor data and subsequent reported fraudulent uses or legitimate uses.Similar techniques may be used to capture signals from a magnetometer,for instance signals indicating variations in an electromagnetic fieldarising from operation of a point-of-sale terminal or related equipment,for instance, emitted due to circuitry within an a handheld scanner ortheft detection system of the store. Or in some cases, variations in amagnetic field (and IMU) may be integrated to infer a user's geolocationmore precisely.

In some embodiments, image signals may be classified, for instance,based on whether the image contains a point-of-sale terminal. In someembodiments, a training set of such images may be captured and manuallylabeled as including such a point-of-sale terminal. Some embodiments maytrain a neural network based on the labeled training set, for example, aconvolution neural network having a convolution layer corresponding tothe portion of the image depicting the point-of-sale terminal. In somecases, a particular part of the point-of-sale terminal may be detected,for instance, a hand-held scanner, which often includes a black screenthat is relatively reliably detected in images or a display of a balancethat may be relatively reliably detected, both with a relativelybandwidth sensitive classifier model. In some cases, the convolutionlayer may be applied multiple times across an image at different,overlapping portions of the image to determine whether output neuron ofthe convolution layer fires, indicating a point-of-sale terminal.

In some embodiments, multiple images may be captured. For example,facial features of the user may be captured in an image taken whensetting up an account on a native mobile application, and later, usingthe techniques discussed above. Some embodiments may determine whetheran image taken with a rear facing camera of the mobile computing deviceincludes the user in the frame, while another image taken with anothercamera facing in the same direction as the screen, includes an image ofa point-of-sale terminal. In another example, light signals emitted by apoint-of-sale terminal in a scanning process may be detected, eitherwith a light sensor or with a camera. For example, a barcode scanner orQR code scanner may emit a laser of a particular frequency (either orboth in electromagnetic frequency and scanning frequency) that may bedetected. In some cases, sensed light intensity may be passed throughserial bandpass filters, such that light flickering at the scan rate ofa barcode scanner, of a color of a scanner laser, is passed through thefilters, and a resulting averaged image intensity over a duration oftime including multiple scans may be compared to a threshold to classifythe image sensor output as indicating the presence of a point-of-saleterminal.

Similar techniques may be used to classify image and time-of-flightsensor outputs. For example, a three-dimensional shape of a portion of apoint-of-sale terminal, like a handheld scanner, may be detected intime-of-flight data, for instance, again, using a convolution layer of atrained neural network to account for translation invariant aspects ofthe signal captured by the mobile computing device.

In some embodiments, a multidimensional time series from the inertialmeasurement unit may be classified as indicating a particular gesturehas occurred or that the mobile computing device is oriented in aparticular direction. For instance, to determine whether the mobilecomputing device is oriented in a particular direction, some embodimentsmay determine whether the signal corresponding to a particular axis ofthe inertial measurement unit (e.g., averaged over some trailingduration of time, like one second) exceeds a threshold, indicating theconsistent pull of gravity in a particular direction, like when thephone is oriented vertically right side up or upside down orhorizontally right side up or upside down.

In some embodiments, a time series of such data may be used to determinewhether a particular gesture has occurred, for instance, indicating thatthe user has rotated and translated the phone through space in a mannerconsistent with how a user typically presents a display screen toanother person entering the information into a point-of-sale terminal,like when a user takes a phone facing towards their face, spins thephone 180° about a vertical axis, translates the phone downward, spinsthe phone 180° about a horizontal axis, and then tilts the phone awayfrom themselves (spinning about an orthogonal horizontal axis), andholds the phone static. In some cases, users may engage in such motionsat different speeds, through different distances and angular changesover time. Accordingly, some embodiments may classify such time seriesas including a qualifying gesture with a dynamic time warp analysis. Forexample, a training set of users may be asked to engage in the gesture,and a template for a dynamic time warp algorithm may be trained, forinstance with dynamic programming and tuned constraints, based on thesensor data from the training exercise. Later, this template may becompared against sensor data obtained when a user request to use a giftcard, and the sensor data may be classified as indicating a gestureassociated with legitimate use.

Next, some embodiments may determine whether the classification scoreexceeds a threshold, as indicated by block 312. In some cases, thisthreshold may be modulated with the techniques described above by whichthe weights for combining the various classification scores arecombined. Upon determining that the score does not exceed the threshold,some embodiments may continue to sense ambient signals, returning thestep 308, in some cases until a timeout determination is made topreserve the battery life of the mobile computing device.

Alternatively, upon determining that the score exceeds a threshold, someembodiments may cause the mobile computing device to display thebalance-access information, as indicated by block 314. In some cases,the balance access information may be decrypted and displayed on adisplay screen of the mobile computing device. In some embodiments, abarcode may be formed from string balance-access information, such as alinear barcode or a QR code, and a resulting image may be displayed,such that the image may be scanned to enter the information into apoint-of-sale terminal. Or in some cases, the information may bedisplayed in human-readable form, such that a salesclerk can type theinformation into a point-of-sale terminal. Or, in some cases, the imageof the barcode or QR code may be formed on the remote server, and theimage may be downloaded, though this use is expected to be higherbandwidth relative to systems that compose such images on the mobilecomputing device, as the string data encoded therein is often much lessdata intensive.

A variety of techniques may be executed to impede users from capturingthe displayed information. Some embodiments may instruct a mobilecomputing device to block the mobile computing device from performing ascreen capture. Some embodiments may display the balance-accessinformation for a threshold amount of time that is relatively short(e.g., less than five seconds, less than one second, or less thanone-half of one second), such that the information may be captured by amachine, but is too quickly removed to be reliably captured by humanbeing. Some embodiments may flash the information on the screen repeatedtimes, such that the scanner has multiple opportunities to capture theinformation, while a human would find it difficult to record theinformation. Some embodiments may animate movement of the code on thescreen to bring the code in and out of focus of a user camera attemptingto capture an image of the code, e.g., exceeding a tracking rate oftypical autofocus mechanisms in consumer cameras, while staying within atracking rate that can be accommodated by point of sale scanners. Someembodiments may compose a plurality of scannable codes, like barcodes,some of which are internally inconsistent and invalid (e.g., dummycodes), and one of which contains the balance-access information in aninternally consistent scannable code. In some cases, formats for somescannable codes include redundancy for purposes of error detection andcorrection, like parity bits. Some embodiments may flash a sequence ofscannable codes in which all but one of the scannable codes in thesequence, for example, a randomized one in the sequence, contain invalidcodes in which the error detection and correction rules are violated,for instance, with an incorrect parity bit. As a result, it is expectedthat a point-of-sale terminal scanning the flashing codes will rejectall but the legitimate code, while a user attempting to write down thecodes will not know which one is legitimate without a much morelaborious effort. In some cases, an entire screen may be varied inintensity in synchronicity with a scanning rate of a barcode scanner,such that the lightness or darkness of the screen varies according towhat a barcode scanner would sense while transiting across a barcode,thereby conveying a signal to the barcode scanner that matches whatwould be perceived by a static one or two dimensional barcode withoutpresenting a static image that a user can readily visually parse.

Thus, with various combinations of the above techniques, users may bedeterred from engaging in fraud.

In some embodiments, the gift card management system of FIG. 1 mayexecute various routines to further reduce the likelihood of fraud. Forinstance, some embodiments may infer register balances and select giftcards to match the inferred balance to expedite gift card exhaustion.For instance, some embodiments may estimate a register balance based ona distribution of previous know balances for a given retailer and forusers deemed to have a profile similar to that of the user requestingcards. Some embodiments may then obtain a set of candidate cards andselect among the candidate cards based on 1) a current balance of thecandidate card; 2) a risk score determined for each candidate card(e.g., based on an amount of users who have had access to the card andwhether those users have a relatively long history of non-fraudulentcard use recorded in the system). Some embodiments may select the cardclosest to the inferred register balance having a risk score above athreshold. Some embodiments may calculate a weighted combination of therisk score and an inverse of the difference between the inferredregister balance (or an actual balance) and the card balance. Someembodiments may rank the cards based on this weighted combined score andselect a highest scoring card. Some embodiments may select a riskiestcard having a balance expected to be exhausted by the inferred registerbalance.

Similarly, some embodiments may suppress the number of cards a useraccesses in a transaction. For instance, a user may combine two or threeor more gift card balances to pay a register balance. The more cardsconsumed and not exhausted, the greater the risk of fraud. Accordingly,some embodiments may rate limit a number of cards a user is allowed toaccess or rate limit an aggregate balance of cards a user can access.Similarly, some embodiments may select among candidate cards to reducethe number provided to a given users, e.g., by favoring cards close tothe inferred (or actual) register balance.

Further, some embodiments may distribute cards geographically to enhancethe power of the purchase location to signal fraud. In some cases, acard may be sent to a first geographic area and returned with a balance.Later, a user in a different geographic area (e.g., more than athreshold distance, or having less than a threshold co-occurrence rateamong users between the two locations) may request a card for the sameretailer. The card for the later user may be selected in response todetermining that this geographic threshold is satisfied. A differentlater user in the first geographic location may not be provided the cardupon determining that the geographic locations are the same. Later, iffraud occurs, the location of the fraud is expected to be indicative ofwhich user holding a given card engaged in fraud, as it is expected tobe less likely that a user will travel to a relatively distantgeographic location to spend a card they previously surrendered, ratherthan attempt to use the same card in their same area.

FIG. 4 is a diagram that illustrates an exemplary computing system 1000in accordance with embodiments of the present technique. Variousportions of systems and methods described herein, may include or beexecuted on one or more computer systems similar to computing system1000. Further, processes and modules described herein may be executed byone or more processing systems similar to that of computing system 1000.

Computing system 1000 may include one or more processors (e.g.,processors 1010 a-1010 n) coupled to system memory 1020, an input/outputI/O device interface 1030, and a network interface 1040 via aninput/output (I/O) interface 1050. A processor may include a singleprocessor or a plurality of processors (e.g., distributed processors). Aprocessor may be any suitable processor capable of executing orotherwise performing instructions. A processor may include a centralprocessing unit (CPU) that carries out program instructions to performthe arithmetical, logical, and input/output operations of computingsystem 1000. A processor may execute code (e.g., processor firmware, aprotocol stack, a database management system, an operating system, or acombination thereof) that creates an execution environment for programinstructions. A processor may include a programmable processor. Aprocessor may include general or special purpose microprocessors. Aprocessor may receive instructions and data from a memory (e.g., systemmemory 1020). Computing system 1000 may be a uni-processor systemincluding one processor (e.g., processor 1010 a), or a multi-processorsystem including any number of suitable processors (e.g., 1010 a-1010n). Multiple processors may be employed to provide for parallel orsequential execution of one or more portions of the techniques describedherein. Processes, such as logic flows, described herein may beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating corresponding output. Processes described herein may beperformed by, and apparatus can also be implemented as, special purposelogic circuitry, e.g., an FPGA (field programmable gate array) or anASIC (application specific integrated circuit). Computing system 1000may include a plurality of computing devices (e.g., distributed computersystems) to implement various processing functions.

I/O device interface 1030 may provide an interface for connection of oneor more I/O devices 1060 to computer system 1000. I/O devices mayinclude devices that receive input (e.g., from a user) or outputinformation (e.g., to a user). I/O devices 1060 may include, forexample, graphical user interface presented on displays (e.g., a cathoderay tube (CRT) or liquid crystal display (LCD) monitor), pointingdevices (e.g., a computer mouse or trackball), keyboards, keypads,touchpads, scanning devices, voice recognition devices, gesturerecognition devices, printers, audio speakers, microphones, cameras, orthe like. I/O devices 1060 may be connected to computer system 1000through a wired or wireless connection. I/O devices 1060 may beconnected to computer system 1000 from a remote location. I/O devices1060 located on remote computer system, for example, may be connected tocomputer system 1000 via a network and network interface 1040.

Network interface 1040 may include a network adapter that provides forconnection of computer system 1000 to a network. Network interface may1040 may facilitate data exchange between computer system 1000 and otherdevices connected to the network. Network interface 1040 may supportwired or wireless communication. The network may include an electroniccommunication network, such as the Internet, a local area network (LAN),a wide area network (WAN), a cellular communications network, or thelike.

System memory 1020 may be configured to store program instructions 1100or data 1110. Program instructions 1100 may be executable by a processor(e.g., one or more of processors 1010 a-1010 n) to implement one or moreembodiments of the present techniques. Instructions 1100 may includemodules of computer program instructions for implementing one or moretechniques described herein with regard to various processing modules.Program instructions may include a computer program (which in certainforms is known as a program, software, software application, script, orcode). A computer program may be written in a programming language,including compiled or interpreted languages, or declarative orprocedural languages. A computer program may include a unit suitable foruse in a computing environment, including as a stand-alone program, amodule, a component, or a subroutine. A computer program may or may notcorrespond to a file in a file system. A program may be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program may be deployed to be executed on one ormore computer processors located locally at one site or distributedacross multiple remote sites and interconnected by a communicationnetwork.

System memory 1020 may include a tangible program carrier having programinstructions stored thereon. A tangible program carrier may include anon-transitory computer readable storage medium. A non-transitorycomputer readable storage medium may include a machine readable storagedevice, a machine readable storage substrate, a memory device, or anycombination thereof. Non-transitory computer readable storage medium mayinclude non-volatile memory (e.g., flash memory, ROM, PROM, EPROM,EEPROM memory), volatile memory (e.g., random access memory (RAM),static random access memory (SRAM), synchronous dynamic RAM (SDRAM)),bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or thelike. System memory 1020 may include a non-transitory computer readablestorage medium that may have program instructions stored thereon thatare executable by a computer processor (e.g., one or more of processors1010 a-1010 n) to cause the subject matter and the functional operationsdescribed herein. A memory (e.g., system memory 1020) may include asingle memory device and/or a plurality of memory devices (e.g.,distributed memory devices). Instructions or other program code toprovide the functionality described herein may be stored on a tangible,non-transitory computer readable media. In some cases, the entire set ofinstructions may be stored concurrently on the media, or in some cases,different parts of the instructions may be stored on the same media atdifferent times, e.g., a copy may be created by writing program code toa first-in-first-out buffer in a network interface, where some of theinstructions are pushed out of the buffer before other portions of theinstructions are written to the buffer, with all of the instructionsresiding in memory on the buffer, just not all at the same time.

I/O interface 1050 may be configured to coordinate I/O traffic betweenprocessors 1010 a-1010 n, system memory 1020, network interface 1040,I/O devices 1060, and/or other peripheral devices. I/O interface 1050may perform protocol, timing, or other data transformations to convertdata signals from one component (e.g., system memory 1020) into a formatsuitable for use by another component (e.g., processors 1010 a-1010 n).I/O interface 1050 may include support for devices attached throughvarious types of peripheral buses, such as a variant of the PeripheralComponent Interconnect (PCI) bus standard or the Universal Serial Bus(USB) standard.

Embodiments of the techniques described herein may be implemented usinga single instance of computer system 1000 or multiple computer systems1000 configured to host different portions or instances of embodiments.Multiple computer systems 1000 may provide for parallel or sequentialprocessing/execution of one or more portions of the techniques describedherein.

Those skilled in the art will appreciate that computer system 1000 ismerely illustrative and is not intended to limit the scope of thetechniques described herein. Computer system 1000 may include anycombination of devices or software that may perform or otherwise providefor the performance of the techniques described herein. For example,computer system 1000 may include or be a combination of acloud-computing system, a data center, a server rack, a server, avirtual server, a desktop computer, a laptop computer, a tabletcomputer, a server device, a client device, a mobile telephone, apersonal digital assistant (PDA), a mobile audio or video player, a gameconsole, a vehicle-mounted computer, or a Global Positioning System(GPS), or the like. Computer system 1000 may also be connected to otherdevices that are not illustrated, or may operate as a stand-alonesystem. In addition, the functionality provided by the illustratedcomponents may in some embodiments be combined in fewer components ordistributed in additional components. Similarly, in some embodiments,the functionality of some of the illustrated components may not beprovided or other additional functionality may be available.

Those skilled in the art will also appreciate that while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1000 may be transmitted to computer system1000 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network or a wireless link. Various embodiments may furtherinclude receiving, sending, or storing instructions or data implementedin accordance with the foregoing description upon a computer-accessiblemedium. Accordingly, the present invention may be practiced with othercomputer system configurations.

In block diagrams, illustrated components are depicted as discretefunctional blocks, but embodiments are not limited to systems in whichthe functionality described herein is organized as illustrated. Thefunctionality provided by each of the components may be provided bysoftware or hardware modules that are differently organized than ispresently depicted, for example such software or hardware may beintermingled, conjoined, replicated, broken up, distributed (e.g. withina data center or geographically), or otherwise differently organized.The functionality described herein may be provided by one or moreprocessors of one or more computers executing code stored on a tangible,non-transitory, machine readable medium. In some cases, third partycontent delivery networks may host some or all of the informationconveyed over networks, in which case, to the extent information (e.g.,content) is said to be supplied or otherwise provided, the informationmay provided by sending instructions to retrieve that information from acontent delivery network.

The reader should appreciate that the present application describesseveral inventions. Rather than separating those inventions intomultiple isolated patent applications, applicants have grouped theseinventions into a single document because their related subject matterlends itself to economies in the application process. But the distinctadvantages and aspects of such inventions should not be conflated. Insome cases, embodiments address all of the deficiencies noted herein,but it should be understood that the inventions are independentlyuseful, and some embodiments address only a subset of such problems oroffer other, unmentioned benefits that will be apparent to those ofskill in the art reviewing the present disclosure. Due to costsconstraints, some inventions disclosed herein may not be presentlyclaimed and may be claimed in later filings, such as continuationapplications or by amending the present claims. Similarly, due to spaceconstraints, neither the Abstract nor the Summary of the Inventionsections of the present document should be taken as containing acomprehensive listing of all such inventions or all aspects of suchinventions.

It should be understood that the description and the drawings are notintended to limit the invention to the particular form disclosed, but tothe contrary, the intention is to cover all modifications, equivalents,and alternatives falling within the spirit and scope of the presentinvention as defined by the appended claims. Further modifications andalternative embodiments of various aspects of the invention will beapparent to those skilled in the art in view of this description.Accordingly, this description and the drawings are to be construed asillustrative only and are for the purpose of teaching those skilled inthe art the general manner of carrying out the invention. It is to beunderstood that the forms of the invention shown and described hereinare to be taken as examples of embodiments. Elements and materials maybe substituted for those illustrated and described herein, parts andprocesses may be reversed or omitted, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims. Headings used herein are for organizational purposesonly and are not meant to be used to limit the scope of the description.

As used throughout this application, the word “may” is used in apermissive sense (i.e., meaning having the potential to), rather thanthe mandatory sense (i.e., meaning must). The words “include”,“including”, and “includes” and the like mean including, but not limitedto. As used throughout this application, the singular forms “a,” “an,”and “the” include plural referents unless the content explicitlyindicates otherwise. Thus, for example, reference to “an element” or “aelement” includes a combination of two or more elements, notwithstandinguse of other terms and phrases for one or more elements, such as “one ormore.” The term “or” is, unless indicated otherwise, non-exclusive,i.e., encompassing both “and” and “or.” Terms describing conditionalrelationships, e.g., “in response to X, Y,” “upon X, Y,”, “if X, Y,”“when X, Y,” and the like, encompass causal relationships in which theantecedent is a necessary causal condition, the antecedent is asufficient causal condition, or the antecedent is a contributory causalcondition of the consequent, e.g., “state X occurs upon condition Yobtaining” is generic to “X occurs solely upon Y” and “X occurs upon Yand Z.” Such conditional relationships are not limited to consequencesthat instantly follow the antecedent obtaining, as some consequences maybe delayed, and in conditional statements, antecedents are connected totheir consequents, e.g., the antecedent is relevant to the likelihood ofthe consequent occurring. Statements in which a plurality of attributesor functions are mapped to a plurality of objects (e.g., one or moreprocessors performing steps A, B, C, and D) encompasses both all suchattributes or functions being mapped to all such objects and subsets ofthe attributes or functions being mapped to subsets of the attributes orfunctions (e.g., both all processors each performing steps A-D, and acase in which processor 1 performs step A, processor 2 performs step Band part of step C, and processor 3 performs part of step C and step D),unless otherwise indicated. Further, unless otherwise indicated,statements that one value or action is “based on” another condition orvalue encompass both instances in which the condition or value is thesole factor and instances in which the condition or value is one factoramong a plurality of factors. Unless otherwise indicated, statementsthat “each” instance of some collection have some property should not beread to exclude cases where some otherwise identical or similar membersof a larger collection do not have the property, i.e., each does notnecessarily mean each and every. Limitations as to sequence of recitedsteps should not be read into the claims unless explicitly specified,e.g., with explicit language like “after performing X, performing Y,” incontrast to statements that might be improperly argued to imply sequencelimitations, like “performing X on items, performing Y on the X'editems,” used for purposes of making claims more readable rather thanspecifying sequence. Unless specifically stated otherwise, as apparentfrom the discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining” or the like refer to actionsor processes of a specific apparatus, such as a special purpose computeror a similar special purpose electronic processing/computing device.

In this patent, certain U.S. patents, U.S. patent applications, or othermaterials (e.g., articles) have been incorporated by reference. The textof such U.S. patents, U.S. patent applications, and other materials is,however, only incorporated by reference to the extent that no conflictexists between such material and the statements and drawings set forthherein. In the event of such conflict, the text of the present documentgoverns.

The present techniques will be better understood with reference to thefollowing enumerated embodiments:

1. A tangible, non-transitory machine-readable media storinginstructions to classify ambient signals to reduce fraudulent use ofstored value card information, wherein the instructions, when executedby one or more processors of a mobile computing device effectuateoperations comprising; receiving, with the mobile computing device, froma remote server, balance-access information by which a stored value cardbalance can be spent at a point of sale terminal; storing, with themobile computing device, the balance-access information; sensing, withone or more sensors of a mobile computing device, ambient signals;classifying the ambient signals as indicating the user is in a retailestablishment; and in response to the classification, displaying, on adisplay screen of the mobile computing device, the balance-accessinformation, such that the balance-access information can be input to apoint-of-sale terminal.2. The media of embodiment 1, wherein: sensing ambient signalscomprises: sensing audio; sensing an image; sensing an orientation; andsensing a wireless beacon; and classifying the ambient signals asindicating the user is in a retail establishment comprises: calculatinga score based on a weighted combination of a classification of thesensed audio, a classification of the image, a classification of theorientation, and a classification of the wireless beacon; anddetermining that the score satisfies a threshold.3. The media of any of embodiments 1-2, comprising: determining ageolocation of the mobile computing device; requesting from a remoteserver parameters of an ambient signal classifier pertinent to thegeolocation; receiving the parameters; and wherein classifying theambient signals comprises classifying the ambient signals based on theparameters.4. The media of embodiment 3, wherein determining a geolocation of themobile computing device comprises: receiving an encrypted rolling codeemitted by a wireless beacon; and validating that the encrypted rollingcode corresponds to wireless beacon of a retailer.5. The media of any of embodiments 1-4, wherein sensing ambient signalscomprises sensing audio with a microphone of the mobile computingdevice.6. The media of embodiment 5, wherein classifying the ambient signalscomprises: normalizing the audio; extracting a feature vector from thenormalized audio; and scoring the feature vector with a value indicatinga likelihood of whether the user is in a retail establishment.7. The media of any of embodiments 5-6, wherein classifying the ambientsignals comprises: extracting a feature by passing a representation ofthe audio through a band-pass filter.8. The media of any of embodiments 5-7, wherein classifying the ambientsignals comprises: extracting a feature by determining a Fouriertransform of a representation of the audio.9. The media of any of embodiments 5-9, wherein the features comprise:an audio signal within a range of frequencies; and duration of the audiosignals within the range of frequencies.10. The media of embodiment 9, wherein the range of frequencies isselected from among a plurality of ranges of frequencies based on ageolocation sensed by the mobile computing device.11. The media of any of embodiments 1-10, wherein sensing ambientsignals comprises receiving a reading from an inertial measurement unit(IMU) of the mobile computing device.12. The media of embodiment 11, wherein classifying the ambient signalscomprises: determining an orientation of the mobile computing devicewith respect to gravity.13. The media of any of embodiments 11-12, wherein classifying theambient signals comprises: classifying a multi-dimensional time seriesof readings from the IMU as indicating a gesture.14. The media of embodiment 13, wherein classifying a multi-dimensionaltime series of readings from the IMU as indicating a gesture comprisesdynamic time warping the multi-dimensional time series.15. The media of any of embodiments 1-14, wherein sensing, with one ormore sensors of a mobile computing device, ambient signals comprises:sensing an image with a camera of the mobile computing device.16. The media of embodiment 15, wherein classifying the ambient signalscomprises: classifying the image as containing at least part of apoint-of-sale terminal.17. The media of any of embodiments 15-16, wherein classifying theambient signals comprises: classifying the image as containing at leastpart of a point-of-sale terminal by detecting translation invariantfeatures in the image corresponding to the point-of-sale terminal with aconvolution layer of a neural network.18. The media of any of embodiments 15-17, wherein classifying theambient signals comprises: classifying two images from two cameras ofthe mobile computing device by determining that one image from a camerafacing in a direction opposite the display screen contains the user'sface, such that the display screen is oriented away from the user.19. The media of any of embodiments 1-18, wherein classifying theambient signals comprises performing steps for classifying the ambientsignals.20. The media of any of embodiments 1-19, wherein displaying, on adisplay screen of the mobile computing device, the balance-accessinformation comprises: receiving an input from a touchscreen of themobile computing device indicating a user input; in response to the userinput, displaying the balance-access information; determining that theuser input has ceased and, in response, ceasing to display the balanceaccess information.21. The media of any of embodiments 1-20, the instructions comprising:determining a geolocation of the mobile computing device; presenting atthe mobile computing device and based on the geolocation, a first giftcard; determining that the first gift card has been utilized in atransaction; and based on a determination that the first gift card hasbeen utilized in the transaction, providing a second gift card at themobile computing device.22. A method, comprising: the operations of any of embodiments 1-21.23. A system, comprising: one or more processors; and memory storinginstructions that when executed by the processors cause the processorsto effectuate operations comprising: the operations of any ofembodiments 1-21.

What is claimed is:
 1. A tangible, non-transitory machine-readable mediastoring instructions to classify ambient signals to reduce fraudulentuse of stored value card information, wherein the instructions, whenexecuted by one or more processors of a mobile computing deviceeffectuate operations comprising: receiving, with the mobile computingdevice, from a remote server, balance-access information by which astored value card balance can be spent at a point of sale terminal;storing, with the mobile computing device, the balance-accessinformation; sensing, with one or more sensors of a mobile computingdevice, ambient signals; classifying the ambient signals as indicatingthe user is in a retail establishment; and in response to theclassification, displaying, on a display screen of the mobile computingdevice, the balance-access information, such that the balance-accessinformation can be input to a point-of-sale terminal, wherein: sensingambient signals comprises sensing audio with a microphone of the mobilecomputing device; and classifying the ambient signals comprises:normalizing the audio; extracting a feature vector from the normalizedaudio; and scoring the feature vector with a value indicating alikelihood of whether the user is in a retail establishment.
 2. Themedia of claim 1, wherein: sensing ambient signals comprises: sensing animage; sensing an orientation; and sensing a wireless beacon; andclassifying the ambient signals as indicating the user is in a retailestablishment comprises: calculating a score based on a weightedcombination of a classification of the sensed audio, a classification ofthe image, a classification of the orientation, and a classification ofthe wireless beacon; and determining that the score satisfies athreshold.
 3. The media of claim 1, the instructions comprising:determining a geolocation of the mobile computing device; requestingfrom a remote server parameters of an ambient signal classifierpertinent to the geolocation; receiving the parameters; and whereinclassifying the ambient signals comprises classifying the ambientsignals based on the parameters.
 4. The media of claim 3, whereindetermining a geolocation of the mobile computing device comprises:receiving an encrypted rolling code emitted by a wireless beacon; andvalidating that the encrypted rolling code corresponds to wirelessbeacon of a retailer.
 5. The media of claim 1, wherein classifying theambient signals comprises: extracting a feature by passing arepresentation of the audio through a band-pass filter.
 6. The media ofclaim 1, wherein classifying the ambient signals comprises: extracting afeature by determining a Fourier transform of a representation of theaudio.
 7. The media of claim 1, wherein the features comprise: an audiosignal within a range of frequencies; and duration of the audio signalswithin the range of frequencies.
 8. The media of claim 7, wherein therange of frequencies is selected from among a plurality of ranges offrequencies based on a geolocation sensed by the mobile computingdevice.
 9. The media of claim 1, wherein sensing ambient signalscomprises receiving a reading from an inertial measurement unit (IMU) ofthe mobile computing device.
 10. The media of claim 9, whereinclassifying the ambient signals comprises: determining an orientation ofthe mobile computing device with respect to gravity.
 11. The media ofclaim 9, wherein classifying the ambient signals comprises: classifyinga multi-dimensional time series of readings from the IMU as indicating agesture.
 12. The media of claim 11, wherein classifying amulti-dimensional time series of readings from the IMU as indicating agesture comprises dynamic time warping the multi-dimensional timeseries.
 13. The media of claim 1, wherein sensing, with one or moresensors of a mobile computing device, ambient signals comprises: sensingan image with a camera of the mobile computing device.
 14. The media ofclaim 13, wherein classifying the ambient signals comprises: classifyingthe image as containing at least part of a point-of-sale terminal. 15.The media of claim 13, wherein classifying the ambient signalscomprises: classifying the image as containing at least part of apoint-of-sale terminal by detecting translation invariant features inthe image corresponding to the point-of-sale terminal with a convolutionlayer of a neural network.
 16. The media of claim 13, whereinclassifying the ambient signals comprises: classifying two images fromtwo cameras of the mobile computing device by determining that one imagefrom a camera facing in a direction opposite the display screen containsthe user's face, such that the display screen is oriented away from theuser.
 17. The media of claim 1, wherein classifying the ambient signalscomprises performing steps for classifying the ambient signals.
 18. Themedia of claim 1, wherein displaying, on a display screen of the mobilecomputing device, the balance-access information comprises: receiving aninput from a touchscreen of the mobile computing device indicating auser input; in response to the user input, displaying the balance-accessinformation; and determining that the user input has ceased and, inresponse, ceasing to display the balance access information.
 19. Themedia of claim 1, the instructions comprising: determining a geolocationof the mobile computing device; presenting at the mobile computingdevice and based on the geolocation, a first gift card; determining thatthe first gift card has been utilized in a transaction; and based on adetermination that the first gift card has been utilized in thetransaction, providing a second gift card at the mobile computingdevice.
 20. A method, comprising: receiving, with the mobile computingdevice, from a remote server, balance-access information by which astored value card balance can be spent at a point of sale terminal;storing, with the mobile computing device, the balance-accessinformation; sensing, with one or more sensors of a mobile computingdevice, ambient signals; classifying the ambient signals as indicatingthe user is in a retail establishment; and in response to theclassification, displaying, on a display screen of the mobile computingdevice, the balance-access information, such that the balance-accessinformation can be input to a point-of-sale terminal, wherein: sensingambient signals comprises sensing audio with a microphone of the mobilecomputing device; and classifying the ambient signals comprises:normalizing the audio; extracting a feature vector from the normalizedaudio; and scoring the feature vector with a value indicating alikelihood of whether the user is in a retail establishment.
 21. Themethod of claim 20, comprising: determining a geolocation of the mobilecomputing device; requesting from a remote server parameters of anambient signal classifier pertinent to the geolocation; receiving theparameters; and wherein classifying the ambient signals comprisesclassifying the ambient signals based on the parameters.
 22. The methodof claim 20, wherein classifying the ambient signals comprises:extracting a feature by passing a representation of the audio through aband-pass filter.
 23. The method of claim 20, wherein classifying theambient signals comprises: extracting a feature by determining a Fouriertransform of a representation of the audio.
 24. The method of claim 20,wherein the features comprise: an audio signal within a range offrequencies; and duration of the audio signals within the range offrequencies.
 25. The method of claim 20, wherein sensing ambient signalscomprises receiving a reading from an inertial measurement unit (IMU) ofthe mobile computing device.
 26. The method of claim 25, whereinclassifying the ambient signals comprises: determining an orientation ofthe mobile computing device with respect to gravity.
 27. The method ofclaim 20, wherein sensing, with one or more sensors of a mobilecomputing device, ambient signals comprises: sensing an image with acamera of the mobile computing device.
 28. The method of claim 27,wherein classifying the ambient signals comprises: classifying the imageas containing at least part of a point-of-sale terminal.
 29. The methodof claim 20, wherein classifying the ambient signals comprisesperforming steps for classifying the ambient signals.
 30. The method ofclaim 20, wherein displaying, on a display screen of the mobilecomputing device, the balance-access information comprises: receiving aninput from a touchscreen of the mobile computing device indicating auser input; in response to the user input, displaying the balance-accessinformation; and determining that the user input has ceased and, inresponse, ceasing to display the balance access information.
 31. Themethod of claim 20, comprising: determining a geolocation of the mobilecomputing device; presenting at the mobile computing device and based onthe geolocation, a first gift card; determining that the first gift cardhas been utilized in a transaction; and based on a determination thatthe first gift card has been utilized in the transaction, providing asecond gift card at the mobile computing device.
 32. A tangible,non-transitory machine-readable media storing instructions to classifyambient signals to reduce fraudulent use of stored value cardinformation, wherein the instructions, when executed by one or moreprocessors of a mobile computing device effectuate operationscomprising: receiving, with the mobile computing device, from a remoteserver, balance-access information by which a stored value card balancecan be spent at a point of sale terminal; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment;and in response to the classification, displaying, on a display screenof the mobile computing device, the balance-access information, suchthat the balance-access information can be input to a point-of-saleterminal, wherein: sensing, with one or more sensors of a mobilecomputing device, ambient signals comprises: sensing an image with acamera of the mobile computing device; and classifying the ambientsignals comprises: classifying the image as containing at least part ofa point-of-sale terminal by detecting translation invariant features inthe image corresponding to the point-of-sale terminal with a convolutionlayer of a neural network.
 33. The media of claim 32, the operationscomprising: determining a geolocation of the mobile computing device;requesting from a remote server parameters of an ambient signalclassifier pertinent to the geolocation; receiving the parameters; andwherein classifying the ambient signals comprises classifying theambient signals based on the parameters.
 34. The media of claim 32,wherein classifying the ambient signals comprises: classifying twoimages from two cameras of the mobile computing device by determiningthat one image from a camera facing in a direction opposite the displayscreen contains the user's face, such that the display screen isoriented away from the user.
 35. A method, comprising: receiving, withthe mobile computing device, from a remote server, balance-accessinformation by which a stored value card balance can be spent at a pointof sale terminal; storing, with the mobile computing device, thebalance-access information; sensing, with one or more sensors of amobile computing device, ambient signals; classifying the ambientsignals as indicating the user is in a retail establishment; and inresponse to the classification, displaying, on a display screen of themobile computing device, the balance-access information, such that thebalance-access information can be input to a point-of-sale terminal,wherein: sensing, with one or more sensors of a mobile computing device,ambient signals comprises: sensing an image with a camera of the mobilecomputing device; and classifying the ambient signals comprises:classifying the image as containing at least part of a point-of-saleterminal by detecting translation invariant features in the imagecorresponding to the point-of-sale terminal with a convolution layer ofa neural network.
 36. The method of claim 35, comprising: determining ageolocation of the mobile computing device; requesting from a remoteserver parameters of an ambient signal classifier pertinent to thegeolocation; receiving the parameters; and wherein classifying theambient signals comprises classifying the ambient signals based on theparameters.
 37. The method of claim 35, wherein classifying the ambientsignals comprises: classifying two images from two cameras of the mobilecomputing device by determining that one image from a camera facing in adirection opposite the display screen contains the user's face, suchthat the display screen is oriented away from the user.
 38. A tangible,non-transitory machine-readable media storing instructions to classifyambient signals to reduce fraudulent use of stored value cardinformation, wherein the instructions, when executed by one or moreprocessors of a mobile computing device effectuate operationscomprising: receiving, with the mobile computing device, from a remoteserver, balance-access information by which a stored value card balancecan be spent at a point of sale terminal; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment;and in response to the classification, displaying, on a display screenof the mobile computing device, the balance-access information, suchthat the balance-access information can be input to a point-of-saleterminal, wherein: sensing, with one or more sensors of a mobilecomputing device, ambient signals comprises: sensing an image with acamera of the mobile computing device; and classifying the ambientsignals comprises: classifying two images from two cameras of the mobilecomputing device by determining that one image from a camera facing in adirection opposite the display screen contains the user's face, suchthat the display screen is oriented away from the user.
 39. The media ofclaim 38, the operations comprising: determining a geolocation of themobile computing device; requesting from a remote server parameters ofan ambient signal classifier pertinent to the geolocation; receiving theparameters; and wherein classifying the ambient signals comprisesclassifying the ambient signals based on the parameters.
 40. The mediaof claim 38, wherein classifying the ambient signals comprises:classifying the image as containing at least part of a point-of-saleterminal.
 41. A method, comprising: receiving, with the mobile computingdevice, from a remote server, balance-access information by which astored value card balance can be spent at a point of sale terminal;storing, with the mobile computing device, the balance-accessinformation; sensing, with one or more sensors of a mobile computingdevice, ambient signals; classifying the ambient signals as indicatingthe user is in a retail establishment; and in response to theclassification, displaying, on a display screen of the mobile computingdevice, the balance-access information, such that the balance-accessinformation can be input to a point-of-sale terminal, wherein: sensing,with one or more sensors of a mobile computing device, ambient signalscomprises: sensing an image with a camera of the mobile computingdevice; and classifying the ambient signals comprises: classifying twoimages from two cameras of the mobile computing device by determiningthat one image from a camera facing in a direction opposite the displayscreen contains the user's face, such that the display screen isoriented away from the user.
 42. The method of claim 41, comprising:determining a geolocation of the mobile computing device; requestingfrom a remote server parameters of an ambient signal classifierpertinent to the geolocation; receiving the parameters; and whereinclassifying the ambient signals comprises classifying the ambientsignals based on the parameters.
 43. The method of claim 41, whereinclassifying the ambient signals comprises: classifying the image ascontaining at least part of a point-of-sale terminal.
 44. A tangible,non-transitory machine-readable media storing instructions to classifyambient signals to reduce fraudulent use of stored value cardinformation, wherein the instructions, when executed by one or moreprocessors of a mobile computing device effectuate operationscomprising: receiving, with the mobile computing device, from a remoteserver, balance-access information by which a stored value card balancecan be spent at a point of sale terminal; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment;and in response to the classification, displaying, on a display screenof the mobile computing device, the balance-access information, suchthat the balance-access information can be input to a point-of-saleterminal, wherein: sensing ambient signals comprises sensing audio witha microphone of the mobile computing device; the features comprise: anaudio signal within a range of frequencies; and duration of the audiosignals within the range of frequencies; and the range of frequencies isselected from among a plurality of ranges of frequencies based on ageolocation sensed by the mobile computing device.
 45. The media ofclaim 44, the operations comprising: determining a geolocation of themobile computing device; requesting from a remote server parameters ofan ambient signal classifier pertinent to the geolocation; receiving theparameters; and wherein classifying the ambient signals comprisesclassifying the ambient signals based on the parameters.
 46. The mediaof claim 44, wherein classifying the ambient signals comprises:extracting a feature by passing a representation of the audio through aband-pass filter.
 47. The media of claim 44, wherein classifying theambient signals comprises: extracting a feature by determining a Fouriertransform of a representation of the audio.
 48. A method, comprising:receiving, with the mobile computing device, from a remote server,balance-access information by which a stored value card balance can bespent at a point of sale terminal; storing, with the mobile computingdevice, the balance-access information; sensing, with one or moresensors of a mobile computing device, ambient signals; classifying theambient signals as indicating the user is in a retail establishment; andin response to the classification, displaying, on a display screen ofthe mobile computing device, the balance-access information, such thatthe balance-access information can be input to a point-of-sale terminal,wherein: sensing ambient signals comprises sensing audio with amicrophone of the mobile computing device; the features comprise: anaudio signal within a range of frequencies; and duration of the audiosignals within the range of frequencies; and the range of frequencies isselected from among a plurality of ranges of frequencies based on ageolocation sensed by the mobile computing device.
 49. The method ofclaim 48, the comprising: determining a geolocation of the mobilecomputing device; requesting from a remote server parameters of anambient signal classifier pertinent to the geolocation; receiving theparameters; and wherein classifying the ambient signals comprisesclassifying the ambient signals based on the parameters.
 50. The methodof claim 48, wherein classifying the ambient signals comprises:extracting a feature by passing a representation of the audio through aband-pass filter.
 51. The method of claim 48, wherein classifying theambient signals comprises: extracting a feature by determining a Fouriertransform of a representation of the audio.
 52. A tangible,non-transitory machine-readable media storing instructions to classifyambient signals to reduce fraudulent use of stored value cardinformation, wherein the instructions, when executed by one or moreprocessors of a mobile computing device effectuate operationscomprising: receiving, with the mobile computing device, from a remoteserver, balance-access information by which a stored value card balancecan be spent at a point of sale terminal; determining a geolocation ofthe mobile computing device, wherein determining a geolocation of themobile computing device comprises: receiving an encrypted rolling codeemitted by a wireless beacon, and validating that the encrypted rollingcode corresponds to wireless beacon of a retailer; requesting from aremote server parameters of an ambient signal classifier pertinent tothe geolocation; receiving the parameters; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment,wherein classifying the ambient signals comprises classifying theambient signals based on the parameters; and in response to theclassification, displaying, on a display screen of the mobile computingdevice, the balance-access information, such that the balance-accessinformation can be input to a point-of-sale terminal.
 53. The media ofclaim 52, wherein sensing ambient signals comprises sensing audio with amicrophone of the mobile computing device.
 54. The media of claim 52,wherein sensing ambient signals comprises receiving a reading from aninertial measurement unit (IMU) of the mobile computing device.
 55. Themedia of claim 52, wherein sensing, with one or more sensors of a mobilecomputing device, ambient signals comprises: sensing an image with acamera of the mobile computing device.
 56. A method, comprising:receiving, with the mobile computing device, from a remote server,balance-access information by which a stored value card balance can bespent at a point of sale terminal; determining a geolocation of themobile computing device, wherein determining a geolocation of the mobilecomputing device comprises: receiving an encrypted rolling code emittedby a wireless beacon, and validating that the encrypted rolling codecorresponds to wireless beacon of a retailer; requesting from a remoteserver parameters of an ambient signal classifier pertinent to thegeolocation; receiving the parameters; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment,wherein classifying the ambient signals comprises classifying theambient signals based on the parameters; and in response to theclassification, displaying, on a display screen of the mobile computingdevice, the balance-access information, such that the balance-accessinformation can be input to a point-of-sale terminal.
 57. The method ofclaim 56, wherein sensing ambient signals comprises sensing audio with amicrophone of the mobile computing device.
 58. The method of claim 56,wherein sensing ambient signals comprises receiving a reading from aninertial measurement unit (IMU) of the mobile computing device.
 59. Themethod of claim 56, wherein sensing, with one or more sensors of amobile computing device, ambient signals comprises: sensing an imagewith a camera of the mobile computing device.
 60. A tangible,non-transitory machine-readable media storing instructions to classifyambient signals to reduce fraudulent use of stored value cardinformation, wherein the instructions, when executed by one or moreprocessors of a mobile computing device effectuate operationscomprising: receiving, with the mobile computing device, from a remoteserver, balance-access information by which a stored value card balancecan be spent at a point of sale terminal; storing, with the mobilecomputing device, the balance-access information; sensing, with one ormore sensors of a mobile computing device, ambient signals; classifyingthe ambient signals as indicating the user is in a retail establishment;and in response to the classification, displaying, on a display screenof the mobile computing device, the balance-access information, suchthat the balance-access information can be input to a point-of-saleterminal, wherein: sensing ambient signals comprises receiving a readingfrom an inertial measurement unit (IMU) of the mobile computing device;and classifying the ambient signals comprises classifying amulti-dimensional time series of readings from the IMU as indicating agesture.
 61. The media of claim 60, wherein classifying the ambientsignals comprises: determining an orientation of the mobile computingdevice with respect to gravity.
 62. The media of claim 60, whereinclassifying a multi-dimensional time series of readings from the IMU asindicating a gesture comprises dynamic time warping themulti-dimensional time series.
 63. A method, comprising: receiving, withthe mobile computing device, from a remote server, balance-accessinformation by which a stored value card balance can be spent at a pointof sale terminal; storing, with the mobile computing device, thebalance-access information; sensing, with one or more sensors of amobile computing device, ambient signals; classifying the ambientsignals as indicating the user is in a retail establishment; and inresponse to the classification, displaying, on a display screen of themobile computing device, the balance-access information, such that thebalance-access information can be input to a point-of-sale terminal,wherein: sensing ambient signals comprises receiving a reading from aninertial measurement unit (IMU) of the mobile computing device; andclassifying the ambient signals comprises classifying amulti-dimensional time series of readings from the IMU as indicating agesture.
 64. The method of claim 63, wherein classifying the ambientsignals comprises: determining an orientation of the mobile computingdevice with respect to gravity.
 65. The method of claim 63, whereinclassifying a multi-dimensional time series of readings from the IMU asindicating a gesture comprises dynamic time warping themulti-dimensional time series.